Supported Feature Selection algorithms

Chi-square (Chi2)

In statistics, the χ2\chi^2χ​2​​ test is applied to test the independence of two even events. Chi-square statistics between every feature variable and the target variable can be applied to Feature Selection. Refer this article for Mathematical details.

Signal Noise Ratio (SNR)

The Signal Noise Ratio (SNR) is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. SNR is defined as ∣μ1−μ2∣/(σ1+σ2)|\mu_{1} - \mu_{2}| / (\sigma_{1} + \sigma_{2})∣μ​1​​−μ​2​​∣/(σ​1​​+σ​2​​), where μk\mu_{k}μ​k​​ is the mean value of the variable in classes kkk, and σk\sigma_{k}σ​k​​ is the standard deviations of the variable in classes kkk. Clearly, features with larger SNR are useful for classification.